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Record W1930037303 · doi:10.1017/cbo9780511521232.003

The problem of lost children

2006· book-chapter· en· W1930037303 on OpenAlexaboutno aff
Edward H. Cornell, Kenneth A. Hill

Bibliographic record

VenueCambridge University Press eBooks · 2006
Typebook-chapter
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsBrotherConverseNova scotiaGeographyArt historyHistoryGenealogyPsychologyArchaeologyLawPolitical science

Abstract

fetched live from OpenAlex

Ken Hill has been living with the ghost of a nine-year-old Nova Scotia boy for over eighteen years. Hill can picture Andrew Warburton as he was on a sunny day in July 1986 when he was enjoying activities near his aunt's rural home. Dressed in swimming trunks, a tank top, and sneakers, he independently set off to meet his older brother at a lake several hundred meters from the house. Although he had previously walked the path with playmates, Andrew disappeared in the forest on his way to the lake. A massive search effort resulted, ultimately involving over 5,000 community volunteers, fire fighters, and military personnel. The police search manager called the nearby university and asked for a psychologist who knew anything about children's spatial behaviour. Professor Ken Hill agreed to meet the search manager at the incident command post. Hill was asked to indicate on a map of the surrounding environment where search efforts should be focused. Hill remembers that he could think of nothing in the sizable literature on the development of children's spatial representation that applied to this problem. It was obvious that Hill had little to offer, and as the co-ordinators of the search continued to converse among themselves, Hill slipped away from the post to join one of the ground search teams. After eight days of the search effort, Andrew Warburton was found dead from hypothermia, approximately 3.2 km from the place where he had last been seen.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.838
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.160
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2006
Admission routes1
Has abstractyes

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